[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1645":3,"related-1645":76},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":22,"classification":23,"search_phrases":55,"slug":56,"view_count":15,"doi":57,"paper":58,"created_at":75},1645,"Modern weather forecasting: mathematical foundations, tools and current state of the art","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40324-026-00443-w","Abstract Modern weather forecasting relies on the integration of observational systems, numerical modeling, data assimilation, high-performance computing, and increasingly artificial intelligence techniques. This paper reviews the scientific and technological foundations of contemporary weather prediction, with particular emphasis on the mathematical and computational tools that support operational forecasting. Weather prediction is based on the numerical solution of the nonlinear partial differential equations governing atmospheric dynamics. The accuracy of these forecasts critically depends on the availability of observations collected from heterogeneous sources, including ground-based networks, weather radars, satellites, aircraft, and other remote sensing platforms. These data are combined with model forecasts through advanced data assimilation techniques, which provide dynamically consistent estimates of the atmospheric state and improve forecast quality. Special attention is devoted to nowcasting, one of the most challenging areas of modern meteorology. At lead times of a few minutes to several hours, rapidly evolving phenomena such as severe thunderstorms, hailstorms, and flash floods require the integration of high-frequency observations, radar extrapolation methods, ensemble prediction techniques, and machine learning approaches. Artificial intelligence is increasingly used to enhance the detection, tracking, and short-term prediction of convective systems, complementing traditional physics-based methodologies. The paper also discusses the requirements of a modern national forecasting system and presents the high-resolution ICON-2I modeling framework operational at the ItaliaMeteo Agency. Particular emphasis is placed on the role of high-performance computing infrastructures, ensemble forecasting systems, and kilometer-scale data assimilation methods. Finally, current challenges and future developments are examined, including convective-scale prediction, uncertainty quantification, exascale computing, digital twins of the atmosphere, and the growing integration of artificial intelligence with numerical weather prediction.","现代天气预报依赖于观测系统、数值模拟、资料同化、高性能计算以及日益增多的人工智能技术的整合。本文回顾了当代天气预报的科学与技术基础，特别强调了支撑业务预报的数学与计算工具。天气预报基于对控制大气动力学的非线性偏微分方程的数值求解。这些预报的准确性关键取决于从异构来源收集的观测资料，包括地面观测网络、天气雷达、卫星、飞机及其他遥感平台。这些数据通过先进的资料同化技术与模式预报相结合，提供动力上一致的大气状态估计，并提升预报质量。特别关注了临近预报，这是现代气象学中最具挑战性的领域之一。在几分钟到几小时的预报时效内，强雷暴、冰雹和突发性洪水等快速演变现象需要高频观测、雷达外推方法、集合预报技术和机器学习方法的整合。人工智能日益被用于增强对流系统的探测、追踪和短期预测，补充传统的基于物理的方法。本文还讨论了现代国家预报系统的需求，并介绍了意大利气象局业务运行的高分辨率ICON-2I模式框架。特别强调了高性能计算基础设施、集合预报系统和公里尺度资料同化方法的作用。最后，审视了当前的挑战和未来发展，包括对流尺度预报、不确定性量化、百亿亿次计算、大气数字孪生，以及人工智能与数值天气预报日益加深的融合。",null,"SeMA Journal","2026-09-02T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"主题为气象预报数学基础，与农业信息化无直接关联，不进入精选。",[19],{"name":10,"url":6},[21],"数值天气预报",[],{"status":24,"locked":14,"version":25,"method":26,"evidence":27},"needs_review","2026-10-22.2","rules",{"candidates":28,"agricultural_evidence":14,"reason":54},[29,39,47],{"code":30,"score":31,"supported":32,"title":33,"abstract":34,"tags":38},"I2",9,true,[],[35,36,37],"机器学习","machine learning","artificial intelligence",[],{"code":40,"score":41,"supported":32,"title":42,"abstract":43,"tags":46},"I3",6,[],[44,45],"遥感","remote sensing",[],{"code":48,"score":49,"supported":32,"title":50,"abstract":51,"tags":53},"I1",3,[],[52],"数字孪生",[],"农业相关性需要复核",[],"Modernweatherforecasting:mathematicalfou-1645","10.1007\u002Fs40324-026-00443-w",{"doi":57,"openalex_id":59,"authors":60,"venue":10,"cited_by_count":15,"oa_url":66,"card":67,"direction":73,"ingested_from":74},"W7204988710",[61,63],{"name":62,"orcid":9},"Carlo Cacciamani",{"name":64,"orcid":65},"Vincenzo Vesprı","https:\u002F\u002Forcid.org\u002F0000-0002-2684-8646","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs40324-026-00443-w.pdf",{"tldr":68,"method":69,"finding":70,"direction":71,"opportunity":72},"综述现代天气预报的数学基础、工具及AI融合现状。","综述数值模型、数据同化、AI及高性能计算等技术。","AI增强对流系统预测，与物理模型互补，提升短临预报能力。","农业人工智能与决策模型","农业领域可借鉴AI与数值模型融合方法，发展作物生长小尺度天气预报与精准农业决策支持系统。","农业遥感与作物表型","openalex","2026-09-04T23:30:33.571799Z",{"total":15,"page":77,"page_size":77,"items":78},1,[]]